Overview

Brought to you by YData

Dataset statistics

Number of variables42
Number of observations612910
Missing cells11176607
Missing cells (%)43.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 GiB
Average record size in memory2.1 KiB

Variable types

Text29
Categorical5
DateTime2
Numeric3
Boolean3

Alerts

relationships.company1.data.type has constant value "company" Constant
relationships.company2.data.type has constant value "company" Constant
amount_normalized is highly overall correlated with financing_type_normalized and 1 other fieldsHigh correlation
financing_type_normalized is highly overall correlated with amount_normalized and 2 other fieldsHigh correlation
headcount is highly overall correlated with financing_type_normalized and 1 other fieldsHigh correlation
product_data.fuzzy_match is highly overall correlated with amount_normalized and 2 other fieldsHigh correlation
human_approved is highly imbalanced (84.6%) Imbalance
planning is highly imbalanced (98.9%) Imbalance
financing_type_tags is highly imbalanced (93.8%) Imbalance
product_data.fuzzy_match is highly imbalanced (79.6%) Imbalance
amount has 571623 (93.3%) missing values Missing
amount_normalized has 571627 (93.3%) missing values Missing
assets has 597248 (97.4%) missing values Missing
award has 593924 (96.9%) missing values Missing
contact has 524634 (85.6%) missing values Missing
event has 594343 (97.0%) missing values Missing
financing_type has 602468 (98.3%) missing values Missing
financing_type_normalized has 610462 (99.6%) missing values Missing
job_title has 541772 (88.4%) missing values Missing
location has 444747 (72.6%) missing values Missing
product has 392072 (64.0%) missing values Missing
product_data.full_text has 392134 (64.0%) missing values Missing
product_data.name has 597801 (97.5%) missing values Missing
product_data.release_type has 586093 (95.6%) missing values Missing
product_data.release_version has 612281 (99.9%) missing values Missing
product_data.fuzzy_match has 392134 (64.0%) missing values Missing
recognition has 587807 (95.9%) missing values Missing
vulnerability has 600409 (98.0%) missing values Missing
relationships.company1.data.id has 14768 (2.4%) missing values Missing
relationships.company1.data.type has 14768 (2.4%) missing values Missing
relationships.company2.data.id has 412313 (67.3%) missing values Missing
relationships.company2.data.type has 412313 (67.3%) missing values Missing
domain has 14768 (2.4%) missing values Missing
company_name has 14875 (2.4%) missing values Missing
ticker has 479215 (78.2%) missing values Missing
amount_normalized is highly skewed (γ1 = 151.4791593) Skewed
headcount is highly skewed (γ1 = 120.2437773) Skewed
Primary_ID has unique values Unique
confidence has 28568 (4.7%) zeros Zeros
headcount has 606682 (99.0%) zeros Zeros

Reproduction

Analysis started2025-08-29 04:07:33.644681
Analysis finished2025-08-29 04:10:24.288923
Duration2 minutes and 50.64 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Primary_ID
Text

Unique 

Distinct612910
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2025-08-29T09:40:24.679413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters22064760
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique612910 ?
Unique (%)100.0%

Sample

1st row0020f127-3470-4cce-8989-1c79f45da217
2nd row009be1ff-6cfb-4e9f-a415-69baf71f47f3
3rd row01444124-7375-4f03-8879-eb8200b31504
4th row031a304c-29ca-415e-a815-e9c915896540
5th row037783ca-f3f7-4782-8a81-df3cae1ac936
ValueCountFrequency (%)
08293af4-4dea-4eb8-a6a3-a925246c4d9a 1
 
< 0.1%
a3d9d83c-47d6-4c5a-b87c-ae3e52484f01 1
 
< 0.1%
0020f127-3470-4cce-8989-1c79f45da217 1
 
< 0.1%
009be1ff-6cfb-4e9f-a415-69baf71f47f3 1
 
< 0.1%
01444124-7375-4f03-8879-eb8200b31504 1
 
< 0.1%
031a304c-29ca-415e-a815-e9c915896540 1
 
< 0.1%
037783ca-f3f7-4782-8a81-df3cae1ac936 1
 
< 0.1%
03d14654-015f-4efa-b986-05a6b032e8ea 1
 
< 0.1%
04143a02-d0a8-4079-97f1-35bc1497bfb9 1
 
< 0.1%
0493a8e0-6cb2-4a0c-9cff-9076252a963d 1
 
< 0.1%
Other values (612900) 612900
> 99.9%
2025-08-29T09:40:25.110178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2451640
 
11.1%
4 1759767
 
8.0%
b 1302685
 
5.9%
8 1302655
 
5.9%
9 1302417
 
5.9%
a 1301566
 
5.9%
f 1152251
 
5.2%
1 1150254
 
5.2%
c 1150153
 
5.2%
e 1149878
 
5.2%
Other values (7) 8041494
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22064760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2451640
 
11.1%
4 1759767
 
8.0%
b 1302685
 
5.9%
8 1302655
 
5.9%
9 1302417
 
5.9%
a 1301566
 
5.9%
f 1152251
 
5.2%
1 1150254
 
5.2%
c 1150153
 
5.2%
e 1149878
 
5.2%
Other values (7) 8041494
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22064760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2451640
 
11.1%
4 1759767
 
8.0%
b 1302685
 
5.9%
8 1302655
 
5.9%
9 1302417
 
5.9%
a 1301566
 
5.9%
f 1152251
 
5.2%
1 1150254
 
5.2%
c 1150153
 
5.2%
e 1149878
 
5.2%
Other values (7) 8041494
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22064760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2451640
 
11.1%
4 1759767
 
8.0%
b 1302685
 
5.9%
8 1302655
 
5.9%
9 1302417
 
5.9%
a 1301566
 
5.9%
f 1152251
 
5.2%
1 1150254
 
5.2%
c 1150153
 
5.2%
e 1149878
 
5.2%
Other values (7) 8041494
36.4%
Distinct603796
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size80.3 MiB
2025-08-29T09:40:25.520559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length461
Median length267
Mean length65.628552
Min length13

Characters and Unicode

Total characters40224396
Distinct characters531
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique596279 ?
Unique (%)97.3%

Sample

1st rowUnipart Manufacturing Group recognized as Transport and Storage sector winner.
2nd rowOOS International received award two safety awards on Jan 1st '18.
3rd rowNWN Corporation received award Global Winner for 2022 Microsoft Meetings, Calling & Devices for Microsoft Teams Partner of the Year Award on Jun 28th '22.
4th rowGrape Solutions Plc. is developing Mobiliti app on Jan 1st '18.
5th rowNWN Corporation launched two new kits, At-Home Essentials and Office Collaboration Room-as-a-Service on Apr 13th '22.
ValueCountFrequency (%)
on 253387
 
4.1%
with 140619
 
2.3%
of 118920
 
1.9%
inc 111918
 
1.8%
launches 108286
 
1.8%
as 97730
 
1.6%
launched 91532
 
1.5%
1st 89146
 
1.5%
partners 85566
 
1.4%
the 76885
 
1.3%
Other values (229409) 4945994
80.8%
2025-08-29T09:40:26.011868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5507371
 
13.7%
e 3222999
 
8.0%
n 2678470
 
6.7%
a 2497542
 
6.2%
t 2332005
 
5.8%
i 2275691
 
5.7%
o 2163941
 
5.4%
r 2090734
 
5.2%
s 1869253
 
4.6%
c 1220028
 
3.0%
Other values (521) 14366362
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40224396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5507371
 
13.7%
e 3222999
 
8.0%
n 2678470
 
6.7%
a 2497542
 
6.2%
t 2332005
 
5.8%
i 2275691
 
5.7%
o 2163941
 
5.4%
r 2090734
 
5.2%
s 1869253
 
4.6%
c 1220028
 
3.0%
Other values (521) 14366362
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40224396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5507371
 
13.7%
e 3222999
 
8.0%
n 2678470
 
6.7%
a 2497542
 
6.2%
t 2332005
 
5.8%
i 2275691
 
5.7%
o 2163941
 
5.4%
r 2090734
 
5.2%
s 1869253
 
4.6%
c 1220028
 
3.0%
Other values (521) 14366362
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40224396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5507371
 
13.7%
e 3222999
 
8.0%
n 2678470
 
6.7%
a 2497542
 
6.2%
t 2332005
 
5.8%
i 2275691
 
5.7%
o 2163941
 
5.4%
r 2090734
 
5.2%
s 1869253
 
4.6%
c 1220028
 
3.0%
Other values (521) 14366362
35.7%

category
Categorical

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
launches
199986 
partners_with
118857 
hires
65431 
invests_into
25274 
recognized_as
25103 
Other values (24)
178259 

Length

Max length27
Median length22
Mean length10.881691
Min length5

Characters and Unicode

Total characters6669497
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrecognized_as
2nd rowreceives_award
3rd rowreceives_award
4th rowis_developing
5th rowlaunches

Common Values

ValueCountFrequency (%)
launches 199986
32.6%
partners_with 118857
19.4%
hires 65431
 
10.7%
invests_into 25274
 
4.1%
recognized_as 25103
 
4.1%
is_developing 20850
 
3.4%
receives_award 18985
 
3.1%
acquires 18181
 
3.0%
invests_into_assets 14405
 
2.4%
has_issues_with 12501
 
2.0%
Other values (19) 93337
15.2%

Length

2025-08-29T09:40:26.094616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
launches 199986
32.6%
partners_with 118857
19.4%
hires 65431
 
10.7%
invests_into 25274
 
4.1%
recognized_as 25103
 
4.1%
is_developing 20850
 
3.4%
receives_award 18985
 
3.1%
acquires 18181
 
3.0%
invests_into_assets 14405
 
2.4%
has_issues_with 12501
 
2.0%
Other values (19) 93337
15.2%

Most occurring characters

ValueCountFrequency (%)
e 828406
12.4%
s 794048
11.9%
n 571724
8.6%
i 517880
 
7.8%
a 513942
 
7.7%
t 454317
 
6.8%
r 427219
 
6.4%
h 420320
 
6.3%
_ 384979
 
5.8%
c 331733
 
5.0%
Other values (15) 1424929
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6669497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 828406
12.4%
s 794048
11.9%
n 571724
8.6%
i 517880
 
7.8%
a 513942
 
7.7%
t 454317
 
6.8%
r 427219
 
6.4%
h 420320
 
6.3%
_ 384979
 
5.8%
c 331733
 
5.0%
Other values (15) 1424929
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6669497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 828406
12.4%
s 794048
11.9%
n 571724
8.6%
i 517880
 
7.8%
a 513942
 
7.7%
t 454317
 
6.8%
r 427219
 
6.4%
h 420320
 
6.3%
_ 384979
 
5.8%
c 331733
 
5.0%
Other values (15) 1424929
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6669497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 828406
12.4%
s 794048
11.9%
n 571724
8.6%
i 517880
 
7.8%
a 513942
 
7.7%
t 454317
 
6.8%
r 427219
 
6.4%
h 420320
 
6.3%
_ 384979
 
5.8%
c 331733
 
5.0%
Other values (15) 1424929
21.4%
Distinct384036
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Memory size9.4 MiB
Minimum2010-01-05 00:00:00+00:00
Maximum2025-07-07 14:52:48+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-29T09:40:26.194221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:26.312233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

confidence
Real number (ℝ)

Zeros 

Distinct10001
Distinct (%)1.6%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.6043837
Minimum0
Maximum1
Zeros28568
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size9.4 MiB
2025-08-29T09:40:26.432612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.012
Q10.45
median0.6489
Q30.7997
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3497

Descriptive statistics

Standard deviation0.27093289
Coefficient of variation (CV)0.44827962
Kurtosis-0.32263711
Mean0.6043837
Median Absolute Deviation (MAD)0.1699
Skewness-0.60381193
Sum370427.98
Variance0.073404633
MonotonicityNot monotonic
2025-08-29T09:40:26.552218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38538
 
6.3%
0 28568
 
4.7%
0.5 1222
 
0.2%
0.6726 1037
 
0.2%
0.6084 979
 
0.2%
0.7529 627
 
0.1%
0.7282 608
 
0.1%
0.6375 588
 
0.1%
0.7347 564
 
0.1%
0.5747 554
 
0.1%
Other values (9991) 539617
88.0%
ValueCountFrequency (%)
0 28568
4.7%
0.0001 24
 
< 0.1%
0.0002 19
 
< 0.1%
0.0003 8
 
< 0.1%
0.0004 24
 
< 0.1%
0.0005 10
 
< 0.1%
0.0006 10
 
< 0.1%
0.0007 20
 
< 0.1%
0.0008 35
 
< 0.1%
0.0009 9
 
< 0.1%
ValueCountFrequency (%)
1 38538
6.3%
0.9999 52
 
< 0.1%
0.9998 41
 
< 0.1%
0.9997 55
 
< 0.1%
0.9996 30
 
< 0.1%
0.9995 48
 
< 0.1%
0.9994 33
 
< 0.1%
0.9993 44
 
< 0.1%
0.9992 27
 
< 0.1%
0.9991 64
 
< 0.1%
Distinct589468
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size178.4 MiB
2025-08-29T09:40:27.020470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length895
Median length554
Mean length162.92406
Min length14

Characters and Unicode

Total characters99857786
Distinct characters955
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique568980 ?
Unique (%)92.8%

Sample

1st rowIn addition to being named the safest organisation in the UK, Unipart Logistics won the British Safety Council Chief Adjudicator Award for achieving the highest-scoring application of the 647 received from around the world, and was named Transport and Storage sector winner.
2nd rowSince then OOS International has been an active member of the IADC and received two safety awards in 2018.
3rd rowAs a result, with nearly 400 nominees from over 100 countries, NWN Corporation is pleased to announce NWN Carousel was recognized as a Global Winner for 2022 Microsoft Meetings, Calling & Devices for Microsoft Teams Partner of the Year Award.
4th rowMVM Mobiliti and Grape Solutions have been working together since 2018 to develop the Mobiliti app, becoming the most downloaded electric car charging app in Hungary, with more than 215,000 charging stations in 39 countries.
5th rowNWN Carousel, the leading integrated cloud communications service provider, today announced two new kits, At-Home Essentials and Office Collaboration Room-as-a-Service, for organizations to manage the accelerating demands of the hybrid workplace with connectivity, security, devices and visual collaboration.
ValueCountFrequency (%)
the 709462
 
4.7%
and 413318
 
2.7%
to 398940
 
2.6%
of 397305
 
2.6%
in 340668
 
2.2%
a 340511
 
2.2%
has 224946
 
1.5%
with 223187
 
1.5%
for 195680
 
1.3%
its 164231
 
1.1%
Other values (383319) 11817085
77.6%
2025-08-29T09:40:27.565700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14609051
14.6%
e 8819245
 
8.8%
a 6788917
 
6.8%
n 6389938
 
6.4%
t 6363283
 
6.4%
i 6146753
 
6.2%
o 5728229
 
5.7%
r 5182646
 
5.2%
s 4646458
 
4.7%
l 3283642
 
3.3%
Other values (945) 31899624
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99857786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
14609051
14.6%
e 8819245
 
8.8%
a 6788917
 
6.8%
n 6389938
 
6.4%
t 6363283
 
6.4%
i 6146753
 
6.2%
o 5728229
 
5.7%
r 5182646
 
5.2%
s 4646458
 
4.7%
l 3283642
 
3.3%
Other values (945) 31899624
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99857786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
14609051
14.6%
e 8819245
 
8.8%
a 6788917
 
6.8%
n 6389938
 
6.4%
t 6363283
 
6.4%
i 6146753
 
6.2%
o 5728229
 
5.7%
r 5182646
 
5.2%
s 4646458
 
4.7%
l 3283642
 
3.3%
Other values (945) 31899624
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99857786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
14609051
14.6%
e 8819245
 
8.8%
a 6788917
 
6.8%
n 6389938
 
6.4%
t 6363283
 
6.4%
i 6146753
 
6.2%
o 5728229
 
5.7%
r 5182646
 
5.2%
s 4646458
 
4.7%
l 3283642
 
3.3%
Other values (945) 31899624
31.9%

human_approved
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
False
599208 
True
 
13702
ValueCountFrequency (%)
False 599208
97.8%
True 13702
 
2.2%
2025-08-29T09:40:27.626849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

planning
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 MiB
False
612335 
True
 
575
ValueCountFrequency (%)
False 612335
99.9%
True 575
 
0.1%
2025-08-29T09:40:27.665707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

amount
Text

Missing 

Distinct11112
Distinct (%)26.9%
Missing571623
Missing (%)93.3%
Memory size24.9 MiB
2025-08-29T09:40:27.875981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length31
Mean length10.522392
Min length2

Characters and Unicode

Total characters434438
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7383 ?
Unique (%)17.9%

Sample

1st row$155,000
2nd row$1m
3rd row8.8 billion baht
4th row$2.5M
5th row$32,000
ValueCountFrequency (%)
million 24157
32.6%
billion 4470
 
6.0%
1 851
 
1.1%
crore 717
 
1.0%
100 674
 
0.9%
10 668
 
0.9%
rs 600
 
0.8%
usd 595
 
0.8%
5 502
 
0.7%
2 493
 
0.7%
Other values (8460) 40382
54.5%
2025-08-29T09:40:30.759777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 59004
13.6%
i 58303
13.4%
$ 34789
 
8.0%
0 33798
 
7.8%
32818
 
7.6%
o 30644
 
7.1%
n 30182
 
6.9%
m 27104
 
6.2%
1 16411
 
3.8%
5 14718
 
3.4%
Other values (77) 96667
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 434438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 59004
13.6%
i 58303
13.4%
$ 34789
 
8.0%
0 33798
 
7.8%
32818
 
7.6%
o 30644
 
7.1%
n 30182
 
6.9%
m 27104
 
6.2%
1 16411
 
3.8%
5 14718
 
3.4%
Other values (77) 96667
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 434438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 59004
13.6%
i 58303
13.4%
$ 34789
 
8.0%
0 33798
 
7.8%
32818
 
7.6%
o 30644
 
7.1%
n 30182
 
6.9%
m 27104
 
6.2%
1 16411
 
3.8%
5 14718
 
3.4%
Other values (77) 96667
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 434438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 59004
13.6%
i 58303
13.4%
$ 34789
 
8.0%
0 33798
 
7.8%
32818
 
7.6%
o 30644
 
7.1%
n 30182
 
6.9%
m 27104
 
6.2%
1 16411
 
3.8%
5 14718
 
3.4%
Other values (77) 96667
22.3%

amount_normalized
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct8246
Distinct (%)20.0%
Missing571627
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean3.4061063 × 1010
Minimum-6 × 109
Maximum7.5 × 1014
Zeros359
Zeros (%)0.1%
Negative1
Negative (%)< 0.1%
Memory size9.4 MiB
2025-08-29T09:40:30.849978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6 × 109
5-th percentile72000
Q14207000
median35000000
Q32.25 × 108
95-th percentile3.6 × 109
Maximum7.5 × 1014
Range7.50006 × 1014
Interquartile range (IQR)2.20793 × 108

Descriptive statistics

Standard deviation4.4385953 × 1012
Coefficient of variation (CV)130.31288
Kurtosis23643.49
Mean3.4061063 × 1010
Median Absolute Deviation (MAD)34700000
Skewness151.47916
Sum1.4061429 × 1015
Variance1.9701128 × 1025
MonotonicityNot monotonic
2025-08-29T09:40:30.964295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000 734
 
0.1%
10000000 595
 
0.1%
50000000 576
 
0.1%
1000000 553
 
0.1%
20000000 548
 
0.1%
1000000000 522
 
0.1%
5000000 485
 
0.1%
200000000 469
 
0.1%
30000000 465
 
0.1%
15000000 443
 
0.1%
Other values (8236) 35893
 
5.9%
(Missing) 571627
93.3%
ValueCountFrequency (%)
-6000000000 1
 
< 0.1%
0 359
0.1%
1000 61
 
< 0.1%
2000 44
 
< 0.1%
3000 28
 
< 0.1%
4000 18
 
< 0.1%
5000 68
 
< 0.1%
6000 17
 
< 0.1%
7000 13
 
< 0.1%
8000 20
 
< 0.1%
ValueCountFrequency (%)
7.5 × 10141
< 0.1%
5 × 10141
< 0.1%
1.5 × 10131
< 0.1%
1.13 × 10132
< 0.1%
9.2 × 10121
< 0.1%
9 × 10122
< 0.1%
6.66 × 10121
< 0.1%
4.2 × 10121
< 0.1%
3.8 × 10121
< 0.1%
3.3 × 10121
< 0.1%

assets
Text

Missing 

Distinct10145
Distinct (%)64.8%
Missing597248
Missing (%)97.4%
Memory size24.1 MiB
2025-08-29T09:40:31.173011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length108
Median length80
Mean length22.466032
Min length2

Characters and Unicode

Total characters351863
Distinct characters113
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8608 ?
Unique (%)55.0%

Sample

1st rowenergy vehicle (NEV) production plant
2nd rowEV production facilities
3rd rowenergy vehicle production facility
4th rowresearch center
5th rowdata center
ValueCountFrequency (%)
in 2296
 
4.6%
stake 2079
 
4.2%
facility 1737
 
3.5%
plant 1380
 
2.8%
and 1308
 
2.6%
center 860
 
1.7%
manufacturing 700
 
1.4%
facilities 655
 
1.3%
of 551
 
1.1%
building 519
 
1.0%
Other values (7931) 37368
75.6%
2025-08-29T09:40:31.478257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33787
 
9.6%
e 31513
 
9.0%
t 27107
 
7.7%
i 26908
 
7.6%
a 26798
 
7.6%
n 22782
 
6.5%
r 21444
 
6.1%
s 18486
 
5.3%
o 17589
 
5.0%
l 15163
 
4.3%
Other values (103) 110286
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 351863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
33787
 
9.6%
e 31513
 
9.0%
t 27107
 
7.7%
i 26908
 
7.6%
a 26798
 
7.6%
n 22782
 
6.5%
r 21444
 
6.1%
s 18486
 
5.3%
o 17589
 
5.0%
l 15163
 
4.3%
Other values (103) 110286
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 351863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
33787
 
9.6%
e 31513
 
9.0%
t 27107
 
7.7%
i 26908
 
7.6%
a 26798
 
7.6%
n 22782
 
6.5%
r 21444
 
6.1%
s 18486
 
5.3%
o 17589
 
5.0%
l 15163
 
4.3%
Other values (103) 110286
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 351863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
33787
 
9.6%
e 31513
 
9.0%
t 27107
 
7.7%
i 26908
 
7.6%
a 26798
 
7.6%
n 22782
 
6.5%
r 21444
 
6.1%
s 18486
 
5.3%
o 17589
 
5.0%
l 15163
 
4.3%
Other values (103) 110286
31.3%
Distinct156
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.7 MiB
2025-08-29T09:40:31.565711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length0
Mean length1.1314565
Min length0

Characters and Unicode

Total characters693481
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th rowoffice
ValueCountFrequency (%)
education 14183
22.8%
research_and_development 10530
17.0%
it 8492
13.7%
production 7265
11.7%
hospitality 4300
 
6.9%
retail 3939
 
6.3%
transportation 3610
 
5.8%
energy 3226
 
5.2%
distribution 2833
 
4.6%
office 2230
 
3.6%
Other values (2) 1465
 
2.4%
2025-08-29T09:40:31.789656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 83807
12.1%
t 70928
10.2%
n 58633
 
8.5%
i 58283
 
8.4%
o 55826
 
8.1%
a 53548
 
7.7%
r 47008
 
6.8%
d 45341
 
6.5%
c 34250
 
4.9%
p 25705
 
3.7%
Other values (13) 160152
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 693481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 83807
12.1%
t 70928
10.2%
n 58633
 
8.5%
i 58283
 
8.4%
o 55826
 
8.1%
a 53548
 
7.7%
r 47008
 
6.8%
d 45341
 
6.5%
c 34250
 
4.9%
p 25705
 
3.7%
Other values (13) 160152
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 693481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 83807
12.1%
t 70928
10.2%
n 58633
 
8.5%
i 58283
 
8.4%
o 55826
 
8.1%
a 53548
 
7.7%
r 47008
 
6.8%
d 45341
 
6.5%
c 34250
 
4.9%
p 25705
 
3.7%
Other values (13) 160152
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 693481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 83807
12.1%
t 70928
10.2%
n 58633
 
8.5%
i 58283
 
8.4%
o 55826
 
8.1%
a 53548
 
7.7%
r 47008
 
6.8%
d 45341
 
6.5%
c 34250
 
4.9%
p 25705
 
3.7%
Other values (13) 160152
23.1%

award
Text

Missing 

Distinct16536
Distinct (%)87.1%
Missing593924
Missing (%)96.9%
Memory size24.7 MiB
2025-08-29T09:40:31.943568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length373
Median length170
Mean length37.589329
Min length4

Characters and Unicode

Total characters713671
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15473 ?
Unique (%)81.5%

Sample

1st rowtwo safety awards
2nd rowGlobal Winner for 2022 Microsoft Meetings, Calling & Devices for Microsoft Teams Partner of the Year Award
3rd rowNational Science and Technology Progress Award
4th rowSMB Partner of the Year
5th rowFaculty of Medicine and Health Sciences
ValueCountFrequency (%)
award 10547
 
9.7%
the 5409
 
5.0%
of 4618
 
4.2%
for 3560
 
3.3%
in 3052
 
2.8%
awards 3020
 
2.8%
year 2847
 
2.6%
best 2498
 
2.3%
excellence 1761
 
1.6%
and 1687
 
1.6%
Other values (10526) 69666
64.1%
2025-08-29T09:40:32.240573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89654
 
12.6%
e 61313
 
8.6%
a 53786
 
7.5%
r 50056
 
7.0%
n 39775
 
5.6%
t 39328
 
5.5%
o 38788
 
5.4%
i 37562
 
5.3%
d 25170
 
3.5%
s 23618
 
3.3%
Other values (131) 254621
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 713671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
89654
 
12.6%
e 61313
 
8.6%
a 53786
 
7.5%
r 50056
 
7.0%
n 39775
 
5.6%
t 39328
 
5.5%
o 38788
 
5.4%
i 37562
 
5.3%
d 25170
 
3.5%
s 23618
 
3.3%
Other values (131) 254621
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 713671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
89654
 
12.6%
e 61313
 
8.6%
a 53786
 
7.5%
r 50056
 
7.0%
n 39775
 
5.6%
t 39328
 
5.5%
o 38788
 
5.4%
i 37562
 
5.3%
d 25170
 
3.5%
s 23618
 
3.3%
Other values (131) 254621
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 713671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
89654
 
12.6%
e 61313
 
8.6%
a 53786
 
7.5%
r 50056
 
7.0%
n 39775
 
5.6%
t 39328
 
5.5%
o 38788
 
5.4%
i 37562
 
5.3%
d 25170
 
3.5%
s 23618
 
3.3%
Other values (131) 254621
35.7%

contact
Text

Missing 

Distinct72593
Distinct (%)82.2%
Missing524634
Missing (%)85.6%
Memory size26.6 MiB
2025-08-29T09:40:32.476426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length183
Median length43
Mean length12.132992
Min length2

Characters and Unicode

Total characters1071052
Distinct characters166
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64152 ?
Unique (%)72.7%

Sample

1st rowJim Sullivan
2nd rowDean Fernandes
3rd rowDarren Leigh
4th rowCriss Edwards
5th rowBryan Jackson CBE
ValueCountFrequency (%)
david 1438
 
0.8%
john 1317
 
0.8%
michael 1068
 
0.6%
mark 868
 
0.5%
chris 754
 
0.4%
paul 752
 
0.4%
james 735
 
0.4%
andrew 686
 
0.4%
scott 647
 
0.4%
mike 620
 
0.4%
Other values (45997) 160490
94.8%
2025-08-29T09:40:32.849219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 102373
 
9.6%
e 94889
 
8.9%
81082
 
7.6%
n 75818
 
7.1%
r 69718
 
6.5%
i 67637
 
6.3%
o 55481
 
5.2%
l 49480
 
4.6%
t 37775
 
3.5%
s 37525
 
3.5%
Other values (156) 399274
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1071052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 102373
 
9.6%
e 94889
 
8.9%
81082
 
7.6%
n 75818
 
7.1%
r 69718
 
6.5%
i 67637
 
6.3%
o 55481
 
5.2%
l 49480
 
4.6%
t 37775
 
3.5%
s 37525
 
3.5%
Other values (156) 399274
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1071052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 102373
 
9.6%
e 94889
 
8.9%
81082
 
7.6%
n 75818
 
7.1%
r 69718
 
6.5%
i 67637
 
6.3%
o 55481
 
5.2%
l 49480
 
4.6%
t 37775
 
3.5%
s 37525
 
3.5%
Other values (156) 399274
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1071052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 102373
 
9.6%
e 94889
 
8.9%
81082
 
7.6%
n 75818
 
7.1%
r 69718
 
6.5%
i 67637
 
6.3%
o 55481
 
5.2%
l 49480
 
4.6%
t 37775
 
3.5%
s 37525
 
3.5%
Other values (156) 399274
37.3%
Distinct5562
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size9.4 MiB
Minimum1916-01-01 00:00:00
Maximum2033-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-29T09:40:32.943761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:33.073669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

event
Text

Missing 

Distinct15277
Distinct (%)82.3%
Missing594343
Missing (%)97.0%
Memory size24.4 MiB
2025-08-29T09:40:33.312940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length152
Median length105
Mean length29.611353
Min length3

Characters and Unicode

Total characters549794
Distinct characters140
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13438 ?
Unique (%)72.4%

Sample

1st rowAustralian Specialist Hub
2nd row2024 Five9 Global Partner Awards
3rd rowAccenture HealthTech Innovation Challenge
4th rowAccenture HealthTech Innovation Challenge
5th row2021 Australian Space Awards
ValueCountFrequency (%)
awards 7090
 
8.8%
2025 3924
 
4.9%
conference 1717
 
2.1%
annual 1547
 
1.9%
2024 1331
 
1.7%
and 961
 
1.2%
918
 
1.1%
world 908
 
1.1%
summit 815
 
1.0%
of 793
 
1.0%
Other values (10822) 60226
75.1%
2025-08-29T09:40:33.656221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61609
 
11.2%
e 39905
 
7.3%
a 37735
 
6.9%
n 35088
 
6.4%
r 30742
 
5.6%
i 27018
 
4.9%
o 26363
 
4.8%
s 24948
 
4.5%
t 23939
 
4.4%
l 17663
 
3.2%
Other values (130) 224784
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 549794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
61609
 
11.2%
e 39905
 
7.3%
a 37735
 
6.9%
n 35088
 
6.4%
r 30742
 
5.6%
i 27018
 
4.9%
o 26363
 
4.8%
s 24948
 
4.5%
t 23939
 
4.4%
l 17663
 
3.2%
Other values (130) 224784
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 549794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
61609
 
11.2%
e 39905
 
7.3%
a 37735
 
6.9%
n 35088
 
6.4%
r 30742
 
5.6%
i 27018
 
4.9%
o 26363
 
4.8%
s 24948
 
4.5%
t 23939
 
4.4%
l 17663
 
3.2%
Other values (130) 224784
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 549794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
61609
 
11.2%
e 39905
 
7.3%
a 37735
 
6.9%
n 35088
 
6.4%
r 30742
 
5.6%
i 27018
 
4.9%
o 26363
 
4.8%
s 24948
 
4.5%
t 23939
 
4.4%
l 17663
 
3.2%
Other values (130) 224784
40.9%

financing_type
Text

Missing 

Distinct1210
Distinct (%)11.6%
Missing602468
Missing (%)98.3%
Memory size23.7 MiB
2025-08-29T09:40:33.856986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length58
Median length51
Mean length10.522122
Min length3

Characters and Unicode

Total characters109872
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique893 ?
Unique (%)8.6%

Sample

1st rowdonations
2nd rowgrant funding
3rd rowgrant
4th rowgrant
5th rowgrant
ValueCountFrequency (%)
funding 2303
 
12.8%
series 1945
 
10.8%
grant 1837
 
10.2%
public 842
 
4.7%
offering 811
 
4.5%
ipo 744
 
4.1%
initial 712
 
4.0%
a 682
 
3.8%
round 610
 
3.4%
seed 516
 
2.9%
Other values (748) 7006
38.9%
2025-08-29T09:40:34.160360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 13650
12.4%
i 11662
 
10.6%
e 10033
 
9.1%
7566
 
6.9%
r 7278
 
6.6%
t 6570
 
6.0%
g 6283
 
5.7%
a 5490
 
5.0%
f 5437
 
4.9%
d 4841
 
4.4%
Other values (63) 31062
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 13650
12.4%
i 11662
 
10.6%
e 10033
 
9.1%
7566
 
6.9%
r 7278
 
6.6%
t 6570
 
6.0%
g 6283
 
5.7%
a 5490
 
5.0%
f 5437
 
4.9%
d 4841
 
4.4%
Other values (63) 31062
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 13650
12.4%
i 11662
 
10.6%
e 10033
 
9.1%
7566
 
6.9%
r 7278
 
6.6%
t 6570
 
6.0%
g 6283
 
5.7%
a 5490
 
5.0%
f 5437
 
4.9%
d 4841
 
4.4%
Other values (63) 31062
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 13650
12.4%
i 11662
 
10.6%
e 10033
 
9.1%
7566
 
6.9%
r 7278
 
6.6%
t 6570
 
6.0%
g 6283
 
5.7%
a 5490
 
5.0%
f 5437
 
4.9%
d 4841
 
4.4%
Other values (63) 31062
28.3%

financing_type_normalized
Categorical

High correlation  Missing 

Distinct28
Distinct (%)1.1%
Missing610462
Missing (%)99.6%
Memory size37.4 MiB
series_a
635 
series_b
503 
seed
453 
series_c
331 
series_d
184 
Other values (23)
342 

Length

Max length12
Median length8
Mean length7.3419118
Min length4

Characters and Unicode

Total characters17973
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowseed
2nd rowseed
3rd rowseries_e
4th rowseries_b
5th rowseries_b

Common Values

ValueCountFrequency (%)
series_a 635
 
0.1%
series_b 503
 
0.1%
seed 453
 
0.1%
series_c 331
 
0.1%
series_d 184
 
< 0.1%
series_e 88
 
< 0.1%
pre_seed 62
 
< 0.1%
series_f 61
 
< 0.1%
pre_series_a 39
 
< 0.1%
series_g 20
 
< 0.1%
Other values (18) 72
 
< 0.1%
(Missing) 610462
99.6%

Length

2025-08-29T09:40:34.250067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
series_a 635
25.9%
series_b 503
20.5%
seed 453
18.5%
series_c 331
13.5%
series_d 184
 
7.5%
series_e 88
 
3.6%
pre_seed 62
 
2.5%
series_f 61
 
2.5%
pre_series_a 39
 
1.6%
series_g 20
 
0.8%
Other values (18) 72
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 5090
28.3%
s 4360
24.3%
r 2034
 
11.3%
_ 2034
 
11.3%
i 1923
 
10.7%
d 706
 
3.9%
a 697
 
3.9%
b 516
 
2.9%
c 340
 
1.9%
p 112
 
0.6%
Other values (9) 161
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5090
28.3%
s 4360
24.3%
r 2034
 
11.3%
_ 2034
 
11.3%
i 1923
 
10.7%
d 706
 
3.9%
a 697
 
3.9%
b 516
 
2.9%
c 340
 
1.9%
p 112
 
0.6%
Other values (9) 161
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5090
28.3%
s 4360
24.3%
r 2034
 
11.3%
_ 2034
 
11.3%
i 1923
 
10.7%
d 706
 
3.9%
a 697
 
3.9%
b 516
 
2.9%
c 340
 
1.9%
p 112
 
0.6%
Other values (9) 161
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5090
28.3%
s 4360
24.3%
r 2034
 
11.3%
_ 2034
 
11.3%
i 1923
 
10.7%
d 706
 
3.9%
a 697
 
3.9%
b 516
 
2.9%
c 340
 
1.9%
p 112
 
0.6%
Other values (9) 161
 
0.9%

financing_type_tags
Categorical

Imbalance 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.3 MiB
589662 
equity
 
12653
grant
 
5684
ipo
 
1298
donation
 
836
Other values (29)
 
2777

Length

Max length26
Median length0
Mean length0.23838247
Min length0

Characters and Unicode

Total characters146107
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
589662
96.2%
equity 12653
 
2.1%
grant 5684
 
0.9%
ipo 1298
 
0.2%
donation 836
 
0.1%
debt 819
 
0.1%
equity, grant 381
 
0.1%
equity, series_a 357
 
0.1%
equity, series_b 273
 
< 0.1%
seed, equity 239
 
< 0.1%
Other values (24) 708
 
0.1%

Length

2025-08-29T09:40:34.348716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
equity 14591
57.8%
grant 6099
24.2%
ipo 1314
 
5.2%
debt 916
 
3.6%
donation 843
 
3.3%
seed 462
 
1.8%
series_a 360
 
1.4%
series_b 274
 
1.1%
series_c 164
 
0.7%
series_d 92
 
0.4%
Other values (7) 111
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 22449
15.4%
e 18438
12.6%
i 17720
12.1%
u 14591
10.0%
q 14591
10.0%
y 14591
10.0%
n 7817
 
5.4%
a 7334
 
5.0%
r 7068
 
4.8%
g 6134
 
4.2%
Other values (13) 15374
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 146107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 22449
15.4%
e 18438
12.6%
i 17720
12.1%
u 14591
10.0%
q 14591
10.0%
y 14591
10.0%
n 7817
 
5.4%
a 7334
 
5.0%
r 7068
 
4.8%
g 6134
 
4.2%
Other values (13) 15374
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 146107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 22449
15.4%
e 18438
12.6%
i 17720
12.1%
u 14591
10.0%
q 14591
10.0%
y 14591
10.0%
n 7817
 
5.4%
a 7334
 
5.0%
r 7068
 
4.8%
g 6134
 
4.2%
Other values (13) 15374
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 146107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 22449
15.4%
e 18438
12.6%
i 17720
12.1%
u 14591
10.0%
q 14591
10.0%
y 14591
10.0%
n 7817
 
5.4%
a 7334
 
5.0%
r 7068
 
4.8%
g 6134
 
4.2%
Other values (13) 15374
10.5%

headcount
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct698
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.549999
Minimum-10
Maximum1750000
Zeros606682
Zeros (%)99.0%
Negative1
Negative (%)< 0.1%
Memory size9.4 MiB
2025-08-29T09:40:34.454875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1750000
Range1750010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5958.167
Coefficient of variation (CV)59.851
Kurtosis20898.392
Mean99.549999
Median Absolute Deviation (MAD)0
Skewness120.24378
Sum61015190
Variance35499754
MonotonicityNot monotonic
2025-08-29T09:40:34.577189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 606682
99.0%
100 408
 
0.1%
200 270
 
< 0.1%
50 213
 
< 0.1%
1000 211
 
< 0.1%
300 203
 
< 0.1%
500 191
 
< 0.1%
400 171
 
< 0.1%
150 136
 
< 0.1%
250 111
 
< 0.1%
Other values (688) 4314
 
0.7%
ValueCountFrequency (%)
-10 1
 
< 0.1%
0 606682
99.0%
1 6
 
< 0.1%
2 6
 
< 0.1%
3 5
 
< 0.1%
4 7
 
< 0.1%
5 5
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1750000 1
 
< 0.1%
1000000 1
 
< 0.1%
950000 1
 
< 0.1%
800000 3
< 0.1%
780000 1
 
< 0.1%
750000 1
 
< 0.1%
742000 1
 
< 0.1%
700000 1
 
< 0.1%
670000 1
 
< 0.1%
665000 1
 
< 0.1%

job_title
Text

Missing 

Distinct33591
Distinct (%)47.2%
Missing541772
Missing (%)88.4%
Memory size27.1 MiB
2025-08-29T09:40:34.813537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length212
Median length144
Mean length27.304928
Min length2

Characters and Unicode

Total characters1942418
Distinct characters111
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28819 ?
Unique (%)40.5%

Sample

1st rowCEO and chairman
2nd rowChief Technology Officer
3rd rowUnipart Group Executive Chairman
4th rowManaging Director for North America
5th rowNon-Executive Chair
ValueCountFrequency (%)
of 19960
 
7.3%
and 13887
 
5.0%
chief 12854
 
4.7%
director 12602
 
4.6%
president 12441
 
4.5%
officer 11366
 
4.1%
vice 8288
 
3.0%
executive 6849
 
2.5%
head 6389
 
2.3%
ceo 5150
 
1.9%
Other values (8129) 165342
60.1%
2025-08-29T09:40:35.133564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 219331
 
11.3%
203959
 
10.5%
i 160195
 
8.2%
r 144525
 
7.4%
n 128951
 
6.6%
a 127942
 
6.6%
o 114724
 
5.9%
t 110419
 
5.7%
c 93579
 
4.8%
s 75885
 
3.9%
Other values (101) 562908
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1942418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 219331
 
11.3%
203959
 
10.5%
i 160195
 
8.2%
r 144525
 
7.4%
n 128951
 
6.6%
a 127942
 
6.6%
o 114724
 
5.9%
t 110419
 
5.7%
c 93579
 
4.8%
s 75885
 
3.9%
Other values (101) 562908
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1942418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 219331
 
11.3%
203959
 
10.5%
i 160195
 
8.2%
r 144525
 
7.4%
n 128951
 
6.6%
a 127942
 
6.6%
o 114724
 
5.9%
t 110419
 
5.7%
c 93579
 
4.8%
s 75885
 
3.9%
Other values (101) 562908
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1942418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 219331
 
11.3%
203959
 
10.5%
i 160195
 
8.2%
r 144525
 
7.4%
n 128951
 
6.6%
a 127942
 
6.6%
o 114724
 
5.9%
t 110419
 
5.7%
c 93579
 
4.8%
s 75885
 
3.9%
Other values (101) 562908
29.0%
Distinct1039
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size40.9 MiB
2025-08-29T09:40:35.227489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length124
Median length0
Mean length3.321672
Min length0

Characters and Unicode

Total characters2035886
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th rowsupport
ValueCountFrequency (%)
directors 35477
21.0%
general_technology 21288
12.6%
information_technology 14300
8.5%
education 14183
 
8.4%
marketing 13893
 
8.2%
support 11366
 
6.7%
management 11107
 
6.6%
finance 10673
 
6.3%
engineering 7584
 
4.5%
sales 5063
 
3.0%
Other values (9) 24115
14.3%
2025-08-29T09:40:35.453614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 222059
10.9%
n 204089
 
10.0%
o 184751
 
9.1%
t 166490
 
8.2%
r 159839
 
7.9%
i 142536
 
7.0%
a 138467
 
6.8%
g 101070
 
5.0%
c 100714
 
4.9%
s 88475
 
4.3%
Other values (14) 527396
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2035886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 222059
10.9%
n 204089
 
10.0%
o 184751
 
9.1%
t 166490
 
8.2%
r 159839
 
7.9%
i 142536
 
7.0%
a 138467
 
6.8%
g 101070
 
5.0%
c 100714
 
4.9%
s 88475
 
4.3%
Other values (14) 527396
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2035886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 222059
10.9%
n 204089
 
10.0%
o 184751
 
9.1%
t 166490
 
8.2%
r 159839
 
7.9%
i 142536
 
7.0%
a 138467
 
6.8%
g 101070
 
5.0%
c 100714
 
4.9%
s 88475
 
4.3%
Other values (14) 527396
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2035886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 222059
10.9%
n 204089
 
10.0%
o 184751
 
9.1%
t 166490
 
8.2%
r 159839
 
7.9%
i 142536
 
7.0%
a 138467
 
6.8%
g 101070
 
5.0%
c 100714
 
4.9%
s 88475
 
4.3%
Other values (14) 527396
25.9%

location
Text

Missing 

Distinct13642
Distinct (%)8.1%
Missing444747
Missing (%)72.6%
Memory size30.5 MiB
2025-08-29T09:40:35.654192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length89
Median length55
Mean length19.30075
Min length2

Characters and Unicode

Total characters3245672
Distinct characters168
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8228 ?
Unique (%)4.9%

Sample

1st rowUnited Kingdom
2nd rowHungary
3rd rowSydney, Australia
4th rowBoston, Massachusetts, United States
5th rowBoston, Massachusetts, United States
ValueCountFrequency (%)
united 83681
 
18.9%
states 67540
 
15.3%
kingdom 14321
 
3.2%
new 13826
 
3.1%
australia 12583
 
2.8%
india 10237
 
2.3%
california 7952
 
1.8%
york 7531
 
1.7%
texas 4959
 
1.1%
canada 4610
 
1.0%
Other values (10402) 215031
48.6%
2025-08-29T09:40:35.972854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 307773
 
9.5%
t 291749
 
9.0%
e 277950
 
8.6%
274104
 
8.4%
i 252300
 
7.8%
n 243016
 
7.5%
s 155516
 
4.8%
d 150219
 
4.6%
, 141693
 
4.4%
o 124331
 
3.8%
Other values (158) 1027021
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3245672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 307773
 
9.5%
t 291749
 
9.0%
e 277950
 
8.6%
274104
 
8.4%
i 252300
 
7.8%
n 243016
 
7.5%
s 155516
 
4.8%
d 150219
 
4.6%
, 141693
 
4.4%
o 124331
 
3.8%
Other values (158) 1027021
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3245672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 307773
 
9.5%
t 291749
 
9.0%
e 277950
 
8.6%
274104
 
8.4%
i 252300
 
7.8%
n 243016
 
7.5%
s 155516
 
4.8%
d 150219
 
4.6%
, 141693
 
4.4%
o 124331
 
3.8%
Other values (158) 1027021
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3245672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 307773
 
9.5%
t 291749
 
9.0%
e 277950
 
8.6%
274104
 
8.4%
i 252300
 
7.8%
n 243016
 
7.5%
s 155516
 
4.8%
d 150219
 
4.6%
, 141693
 
4.4%
o 124331
 
3.8%
Other values (158) 1027021
31.6%

product
Text

Missing 

Distinct203976
Distinct (%)92.4%
Missing392072
Missing (%)64.0%
Memory size36.7 MiB
2025-08-29T09:40:36.243338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length332
Median length198
Mean length33.377757
Min length1

Characters and Unicode

Total characters7371077
Distinct characters430
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194141 ?
Unique (%)87.9%

Sample

1st rowMobiliti app
2nd rowtwo new kits, At-Home Essentials and Office Collaboration Room-as-a-Service
3rd rowModel 3800
4th rowShare Purchase Plan
5th rowmajor assay program at Minbrie
ValueCountFrequency (%)
for 29562
 
2.7%
of 26489
 
2.4%
the 26311
 
2.4%
and 22056
 
2.0%
to 15337
 
1.4%
in 12190
 
1.1%
program 7900
 
0.7%
on 7706
 
0.7%
new 7108
 
0.7%
series 7032
 
0.6%
Other values (95507) 924505
85.1%
2025-08-29T09:40:36.637145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
865914
 
11.7%
e 672364
 
9.1%
a 472125
 
6.4%
o 469662
 
6.4%
i 466851
 
6.3%
t 449310
 
6.1%
r 444828
 
6.0%
n 414583
 
5.6%
s 341892
 
4.6%
l 265735
 
3.6%
Other values (420) 2507813
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7371077
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
865914
 
11.7%
e 672364
 
9.1%
a 472125
 
6.4%
o 469662
 
6.4%
i 466851
 
6.3%
t 449310
 
6.1%
r 444828
 
6.0%
n 414583
 
5.6%
s 341892
 
4.6%
l 265735
 
3.6%
Other values (420) 2507813
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7371077
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
865914
 
11.7%
e 672364
 
9.1%
a 472125
 
6.4%
o 469662
 
6.4%
i 466851
 
6.3%
t 449310
 
6.1%
r 444828
 
6.0%
n 414583
 
5.6%
s 341892
 
4.6%
l 265735
 
3.6%
Other values (420) 2507813
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7371077
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
865914
 
11.7%
e 672364
 
9.1%
a 472125
 
6.4%
o 469662
 
6.4%
i 466851
 
6.3%
t 449310
 
6.1%
r 444828
 
6.0%
n 414583
 
5.6%
s 341892
 
4.6%
l 265735
 
3.6%
Other values (420) 2507813
34.0%
Distinct205544
Distinct (%)93.1%
Missing392134
Missing (%)64.0%
Memory size37.2 MiB
2025-08-29T09:40:36.905568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length332
Median length203
Mean length35.405116
Min length2

Characters and Unicode

Total characters7816600
Distinct characters433
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196702 ?
Unique (%)89.1%

Sample

1st rowMobiliti app
2nd rowtwo new kits, At-Home Essentials and Office Collaboration Room-as-a-Service
3rd rowModel 3800
4th rowShare Purchase Plan
5th rowmajor assay program at Minbrie
ValueCountFrequency (%)
for 30912
 
2.7%
the 28365
 
2.5%
of 27854
 
2.4%
and 22906
 
2.0%
to 15893
 
1.4%
in 12532
 
1.1%
a 8867
 
0.8%
program 8464
 
0.7%
new 8135
 
0.7%
on 8030
 
0.7%
Other values (97436) 976985
85.0%
2025-08-29T09:40:37.286777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
928574
 
11.9%
e 715179
 
9.1%
a 504107
 
6.4%
o 495616
 
6.3%
i 493741
 
6.3%
t 478633
 
6.1%
r 470119
 
6.0%
n 437772
 
5.6%
s 360718
 
4.6%
l 287397
 
3.7%
Other values (423) 2644744
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7816600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
928574
 
11.9%
e 715179
 
9.1%
a 504107
 
6.4%
o 495616
 
6.3%
i 493741
 
6.3%
t 478633
 
6.1%
r 470119
 
6.0%
n 437772
 
5.6%
s 360718
 
4.6%
l 287397
 
3.7%
Other values (423) 2644744
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7816600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
928574
 
11.9%
e 715179
 
9.1%
a 504107
 
6.4%
o 495616
 
6.3%
i 493741
 
6.3%
t 478633
 
6.1%
r 470119
 
6.0%
n 437772
 
5.6%
s 360718
 
4.6%
l 287397
 
3.7%
Other values (423) 2644744
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7816600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
928574
 
11.9%
e 715179
 
9.1%
a 504107
 
6.4%
o 495616
 
6.3%
i 493741
 
6.3%
t 478633
 
6.1%
r 470119
 
6.0%
n 437772
 
5.6%
s 360718
 
4.6%
l 287397
 
3.7%
Other values (423) 2644744
33.8%

product_data.name
Text

Missing 

Distinct14025
Distinct (%)92.8%
Missing597801
Missing (%)97.5%
Memory size24.0 MiB
2025-08-29T09:40:37.509679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length185
Median length105
Mean length15.293137
Min length1

Characters and Unicode

Total characters231064
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13209 ?
Unique (%)87.4%

Sample

1st rowStronger Through MentHERship
2nd rowFieldbook
3rd rowAsk the Doctor
4th rowUnipart Signite
5th rowSUVs
ValueCountFrequency (%)
the 1387
 
3.8%
of 413
 
1.1%
for 398
 
1.1%
market 329
 
0.9%
and 307
 
0.8%
in 214
 
0.6%
to 213
 
0.6%
a 181
 
0.5%
on 124
 
0.3%
ai 109
 
0.3%
Other values (14719) 32516
89.8%
2025-08-29T09:40:37.868792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21205
 
9.2%
e 20608
 
8.9%
a 14434
 
6.2%
i 13116
 
5.7%
o 12999
 
5.6%
r 12683
 
5.5%
t 12347
 
5.3%
n 11407
 
4.9%
s 8614
 
3.7%
l 7652
 
3.3%
Other values (92) 95999
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 231064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21205
 
9.2%
e 20608
 
8.9%
a 14434
 
6.2%
i 13116
 
5.7%
o 12999
 
5.6%
r 12683
 
5.5%
t 12347
 
5.3%
n 11407
 
4.9%
s 8614
 
3.7%
l 7652
 
3.3%
Other values (92) 95999
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 231064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21205
 
9.2%
e 20608
 
8.9%
a 14434
 
6.2%
i 13116
 
5.7%
o 12999
 
5.6%
r 12683
 
5.5%
t 12347
 
5.3%
n 11407
 
4.9%
s 8614
 
3.7%
l 7652
 
3.3%
Other values (92) 95999
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 231064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21205
 
9.2%
e 20608
 
8.9%
a 14434
 
6.2%
i 13116
 
5.7%
o 12999
 
5.6%
r 12683
 
5.5%
t 12347
 
5.3%
n 11407
 
4.9%
s 8614
 
3.7%
l 7652
 
3.3%
Other values (92) 95999
41.5%
Distinct539
Distinct (%)2.0%
Missing586093
Missing (%)95.6%
Memory size24.2 MiB
2025-08-29T09:40:38.012428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.7260693
Min length4

Characters and Unicode

Total characters207190
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique343 ?
Unique (%)1.3%

Sample

1st rowfeatures
2nd rowmodels
3rd rowsection
4th rowcollection
5th rowfeature
ValueCountFrequency (%)
version 4471
14.7%
line 2664
 
8.8%
feature 2272
 
7.5%
model 2227
 
7.3%
update 2066
 
6.8%
edition 1970
 
6.5%
generation 1934
 
6.4%
features 1485
 
4.9%
collection 1353
 
4.4%
list 1278
 
4.2%
Other values (360) 8703
28.6%
2025-08-29T09:40:38.257913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 35556
17.2%
i 18335
8.8%
n 18315
8.8%
o 16438
 
7.9%
t 16354
 
7.9%
r 13005
 
6.3%
s 12969
 
6.3%
a 12324
 
5.9%
l 11645
 
5.6%
d 11000
 
5.3%
Other values (25) 41249
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 207190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 35556
17.2%
i 18335
8.8%
n 18315
8.8%
o 16438
 
7.9%
t 16354
 
7.9%
r 13005
 
6.3%
s 12969
 
6.3%
a 12324
 
5.9%
l 11645
 
5.6%
d 11000
 
5.3%
Other values (25) 41249
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 207190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 35556
17.2%
i 18335
8.8%
n 18315
8.8%
o 16438
 
7.9%
t 16354
 
7.9%
r 13005
 
6.3%
s 12969
 
6.3%
a 12324
 
5.9%
l 11645
 
5.6%
d 11000
 
5.3%
Other values (25) 41249
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 207190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 35556
17.2%
i 18335
8.8%
n 18315
8.8%
o 16438
 
7.9%
t 16354
 
7.9%
r 13005
 
6.3%
s 12969
 
6.3%
a 12324
 
5.9%
l 11645
 
5.6%
d 11000
 
5.3%
Other values (25) 41249
19.9%
Distinct326
Distinct (%)51.8%
Missing612281
Missing (%)99.9%
Memory size23.4 MiB
2025-08-29T09:40:38.470115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length11
Mean length3.5532591
Min length1

Characters and Unicode

Total characters2235
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)37.8%

Sample

1st row4.00
2nd row18.3
3rd row1.57.0
4th row1.0
5th row3.0
ValueCountFrequency (%)
2.0 45
 
7.2%
3.0 23
 
3.7%
1.0 16
 
2.5%
5 15
 
2.4%
2 13
 
2.1%
2022 12
 
1.9%
1.1 10
 
1.6%
9 10
 
1.6%
3 10
 
1.6%
2020 9
 
1.4%
Other values (315) 466
74.1%
2025-08-29T09:40:38.784410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 585
26.2%
2 340
15.2%
1 330
14.8%
0 327
14.6%
3 138
 
6.2%
4 121
 
5.4%
5 114
 
5.1%
6 75
 
3.4%
7 70
 
3.1%
8 68
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 585
26.2%
2 340
15.2%
1 330
14.8%
0 327
14.6%
3 138
 
6.2%
4 121
 
5.4%
5 114
 
5.1%
6 75
 
3.4%
7 70
 
3.1%
8 68
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 585
26.2%
2 340
15.2%
1 330
14.8%
0 327
14.6%
3 138
 
6.2%
4 121
 
5.4%
5 114
 
5.1%
6 75
 
3.4%
7 70
 
3.1%
8 68
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 585
26.2%
2 340
15.2%
1 330
14.8%
0 327
14.6%
3 138
 
6.2%
4 121
 
5.4%
5 114
 
5.1%
6 75
 
3.4%
7 70
 
3.1%
8 68
 
3.0%

product_data.fuzzy_match
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing392134
Missing (%)64.0%
Memory size24.2 MiB
True
213734 
False
 
7042
(Missing)
392134 
ValueCountFrequency (%)
True 213734
34.9%
False 7042
 
1.1%
(Missing) 392134
64.0%
2025-08-29T09:40:38.835604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct580
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.4 MiB
2025-08-29T09:40:38.916887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length83
Median length0
Mean length2.3865543
Min length0

Characters and Unicode

Total characters1462743
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th rowmobile, online_technology
5th row
ValueCountFrequency (%)
online_technology 25132
20.6%
general_technology 21288
17.5%
marketing 13893
11.4%
campaigns 12817
10.5%
mobile 11476
9.4%
future_tech 8967
 
7.4%
report 6872
 
5.6%
video 5093
 
4.2%
data 3199
 
2.6%
games 3089
 
2.5%
Other values (8) 9979
 
8.2%
2025-08-29T09:40:39.147014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 183440
12.5%
n 148690
 
10.2%
o 143507
 
9.8%
l 107660
 
7.4%
g 97668
 
6.7%
t 96057
 
6.6%
a 77951
 
5.3%
i 77554
 
5.3%
c 73911
 
5.1%
r 63525
 
4.3%
Other values (14) 392780
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 183440
12.5%
n 148690
 
10.2%
o 143507
 
9.8%
l 107660
 
7.4%
g 97668
 
6.7%
t 96057
 
6.6%
a 77951
 
5.3%
i 77554
 
5.3%
c 73911
 
5.1%
r 63525
 
4.3%
Other values (14) 392780
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 183440
12.5%
n 148690
 
10.2%
o 143507
 
9.8%
l 107660
 
7.4%
g 97668
 
6.7%
t 96057
 
6.6%
a 77951
 
5.3%
i 77554
 
5.3%
c 73911
 
5.1%
r 63525
 
4.3%
Other values (14) 392780
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 183440
12.5%
n 148690
 
10.2%
o 143507
 
9.8%
l 107660
 
7.4%
g 97668
 
6.7%
t 96057
 
6.6%
a 77951
 
5.3%
i 77554
 
5.3%
c 73911
 
5.1%
r 63525
 
4.3%
Other values (14) 392780
26.9%

recognition
Text

Missing 

Distinct22237
Distinct (%)88.6%
Missing587807
Missing (%)95.9%
Memory size25.5 MiB
2025-08-29T09:40:39.362435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length429
Median length204
Mean length45.497789
Min length2

Characters and Unicode

Total characters1142131
Distinct characters184
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20959 ?
Unique (%)83.5%

Sample

1st rowTransport and Storage sector winner
2nd rowBest Place to Work
3rd rowtop 50 highest growth companies in Massachusetts
4th row2025 Partner of the Year for Community Impact
5th rowone of two APAC region finalists
ValueCountFrequency (%)
the 13966
 
7.5%
in 11211
 
6.0%
of 8982
 
4.8%
best 6681
 
3.6%
for 6178
 
3.3%
top 5483
 
2.9%
one 3118
 
1.7%
year 3107
 
1.7%
and 2613
 
1.4%
leader 1797
 
1.0%
Other values (12032) 124077
66.3%
2025-08-29T09:40:39.707016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
162056
14.2%
e 103455
 
9.1%
o 77006
 
6.7%
t 76476
 
6.7%
n 69086
 
6.0%
r 67146
 
5.9%
i 66465
 
5.8%
a 64227
 
5.6%
s 54826
 
4.8%
l 33294
 
2.9%
Other values (174) 368094
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1142131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
162056
14.2%
e 103455
 
9.1%
o 77006
 
6.7%
t 76476
 
6.7%
n 69086
 
6.0%
r 67146
 
5.9%
i 66465
 
5.8%
a 64227
 
5.6%
s 54826
 
4.8%
l 33294
 
2.9%
Other values (174) 368094
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1142131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
162056
14.2%
e 103455
 
9.1%
o 77006
 
6.7%
t 76476
 
6.7%
n 69086
 
6.0%
r 67146
 
5.9%
i 66465
 
5.8%
a 64227
 
5.6%
s 54826
 
4.8%
l 33294
 
2.9%
Other values (174) 368094
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1142131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
162056
14.2%
e 103455
 
9.1%
o 77006
 
6.7%
t 76476
 
6.7%
n 69086
 
6.0%
r 67146
 
5.9%
i 66465
 
5.8%
a 64227
 
5.6%
s 54826
 
4.8%
l 33294
 
2.9%
Other values (174) 368094
32.2%

vulnerability
Text

Missing 

Distinct9657
Distinct (%)77.2%
Missing600409
Missing (%)98.0%
Memory size24.2 MiB
2025-08-29T09:40:39.873192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length297
Median length140
Mean length40.645548
Min length4

Characters and Unicode

Total characters508110
Distinct characters162
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9110 ?
Unique (%)72.9%

Sample

1st rowdisasters and ransomware attacks
2nd rowterrorist attack
3rd rowpossible securities law violations
4th rowsevere breathing disorder
5th rowcyber attack
ValueCountFrequency (%)
of 4104
 
5.6%
and 1812
 
2.5%
the 1616
 
2.2%
to 1364
 
1.9%
breach 1251
 
1.7%
attack 1038
 
1.4%
its 1024
 
1.4%
practices 872
 
1.2%
violations 823
 
1.1%
in 801
 
1.1%
Other values (9203) 58278
79.9%
2025-08-29T09:40:40.182196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60471
11.9%
e 44405
 
8.7%
a 41739
 
8.2%
i 38154
 
7.5%
t 37146
 
7.3%
r 30449
 
6.0%
o 29253
 
5.8%
n 28856
 
5.7%
s 28709
 
5.7%
l 24442
 
4.8%
Other values (152) 144486
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 508110
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60471
11.9%
e 44405
 
8.7%
a 41739
 
8.2%
i 38154
 
7.5%
t 37146
 
7.3%
r 30449
 
6.0%
o 29253
 
5.8%
n 28856
 
5.7%
s 28709
 
5.7%
l 24442
 
4.8%
Other values (152) 144486
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 508110
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60471
11.9%
e 44405
 
8.7%
a 41739
 
8.2%
i 38154
 
7.5%
t 37146
 
7.3%
r 30449
 
6.0%
o 29253
 
5.8%
n 28856
 
5.7%
s 28709
 
5.7%
l 24442
 
4.8%
Other values (152) 144486
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 508110
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60471
11.9%
e 44405
 
8.7%
a 41739
 
8.2%
i 38154
 
7.5%
t 37146
 
7.3%
r 30449
 
6.0%
o 29253
 
5.8%
n 28856
 
5.7%
s 28709
 
5.7%
l 24442
 
4.8%
Other values (152) 144486
28.4%
Distinct93622
Distinct (%)15.7%
Missing14768
Missing (%)2.4%
Memory size58.2 MiB
2025-08-29T09:40:40.373403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters21533112
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63872 ?
Unique (%)10.7%

Sample

1st row000bd323-1bf8-5c7a-9941-e6c155c29d10
2nd row000ff896-4292-5b15-9c81-8bf4d76c10d7
3rd row000d8a9c-882c-57f2-8b4c-2afc786d0fa1
4th row0008b75f-9d15-54ae-b70a-52301945e397
5th row000d8a9c-882c-57f2-8b4c-2afc786d0fa1
ValueCountFrequency (%)
c9b92ffd-a2ed-5787-8b06-f4bf8d291e57 13888
 
2.3%
7b7dbf17-a2ad-54cc-ac66-20995d7f6fba 9156
 
1.5%
c5fbb072-cd84-558e-bec2-83c92923d638 3590
 
0.6%
d1667f69-ea53-5059-9f52-39fd8eb4696c 2462
 
0.4%
f6054cea-799a-55b6-83f5-a6efd07ce108 2441
 
0.4%
f0124b95-85b2-53c6-8b39-21f4b3704e31 2310
 
0.4%
b53ca40e-954c-5aed-8e1c-27533975d94a 2155
 
0.4%
0a2cb7a4-6a6b-5a3c-b1bc-edbe4dafe698 1709
 
0.3%
78217424-aa3a-52b9-8b79-29f3eef99218 1577
 
0.3%
02c951c6-a850-5b07-8c80-07e086d6a30f 1575
 
0.3%
Other values (93612) 557279
93.2%
2025-08-29T09:40:40.651455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2392568
 
11.1%
5 1679166
 
7.8%
b 1347783
 
6.3%
8 1285184
 
6.0%
9 1275989
 
5.9%
a 1231024
 
5.7%
f 1188875
 
5.5%
0 1186341
 
5.5%
d 1155629
 
5.4%
7 1154363
 
5.4%
Other values (7) 7636190
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21533112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2392568
 
11.1%
5 1679166
 
7.8%
b 1347783
 
6.3%
8 1285184
 
6.0%
9 1275989
 
5.9%
a 1231024
 
5.7%
f 1188875
 
5.5%
0 1186341
 
5.5%
d 1155629
 
5.4%
7 1154363
 
5.4%
Other values (7) 7636190
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21533112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2392568
 
11.1%
5 1679166
 
7.8%
b 1347783
 
6.3%
8 1285184
 
6.0%
9 1275989
 
5.9%
a 1231024
 
5.7%
f 1188875
 
5.5%
0 1186341
 
5.5%
d 1155629
 
5.4%
7 1154363
 
5.4%
Other values (7) 7636190
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21533112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2392568
 
11.1%
5 1679166
 
7.8%
b 1347783
 
6.3%
8 1285184
 
6.0%
9 1275989
 
5.9%
a 1231024
 
5.7%
f 1188875
 
5.5%
0 1186341
 
5.5%
d 1155629
 
5.4%
7 1154363
 
5.4%
Other values (7) 7636190
35.5%

relationships.company1.data.type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing14768
Missing (%)2.4%
Memory size42.0 MiB
company
598142 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters4186994
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompany
2nd rowcompany
3rd rowcompany
4th rowcompany
5th rowcompany

Common Values

ValueCountFrequency (%)
company 598142
97.6%
(Missing) 14768
 
2.4%

Length

2025-08-29T09:40:40.727121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-29T09:40:40.777580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
company 598142
100.0%

Most occurring characters

ValueCountFrequency (%)
c 598142
14.3%
o 598142
14.3%
m 598142
14.3%
p 598142
14.3%
a 598142
14.3%
n 598142
14.3%
y 598142
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4186994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 598142
14.3%
o 598142
14.3%
m 598142
14.3%
p 598142
14.3%
a 598142
14.3%
n 598142
14.3%
y 598142
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4186994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 598142
14.3%
o 598142
14.3%
m 598142
14.3%
p 598142
14.3%
a 598142
14.3%
n 598142
14.3%
y 598142
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4186994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 598142
14.3%
o 598142
14.3%
m 598142
14.3%
p 598142
14.3%
a 598142
14.3%
n 598142
14.3%
y 598142
14.3%
Distinct577277
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size59.0 MiB
2025-08-29T09:40:41.107835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters22064760
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique545374 ?
Unique (%)89.0%

Sample

1st rowd172abc1-3755-4cef-946e-7de944806e7d
2nd row58c0d5fd-068d-4bab-8ac4-47e19bbdf091
3rd rowef330a38-8624-41c1-8b75-d1b96e7dbd45
4th row0525807d-6ff6-44a0-9c36-8be3afceba5b
5th row16061c55-111d-496a-9e3e-837dddc3454b
ValueCountFrequency (%)
13d038e6-ecaf-4a2f-8ad7-2fd093f8d090 11
 
< 0.1%
a0711e5b-a9c0-49e4-9744-9003a5749d25 10
 
< 0.1%
d904a811-ac52-4c51-9d06-5d01053fe74d 9
 
< 0.1%
db9fd525-b9bb-4702-beae-0e1c49f35458 8
 
< 0.1%
ce230631-885d-4de9-b998-c329373caef2 8
 
< 0.1%
ea5a9ab0-7f09-4b5c-a9b6-3e3d538ddd01 8
 
< 0.1%
2dfc4cd6-aa00-41fb-9dde-773e92e0385e 7
 
< 0.1%
1a95db5e-bcdf-4c8a-9e5e-16a5572c2449 7
 
< 0.1%
ad3cad1e-7a01-48a8-9ade-8845235dda3a 7
 
< 0.1%
24d07b45-24ea-4a5b-9658-ab2109426c19 7
 
< 0.1%
Other values (577267) 612828
> 99.9%
2025-08-29T09:40:41.537506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2451640
 
11.1%
4 1762099
 
8.0%
b 1303896
 
5.9%
8 1303515
 
5.9%
a 1303513
 
5.9%
9 1302294
 
5.9%
1 1150222
 
5.2%
d 1149956
 
5.2%
f 1149706
 
5.2%
e 1149238
 
5.2%
Other values (7) 8038681
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22064760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 2451640
 
11.1%
4 1762099
 
8.0%
b 1303896
 
5.9%
8 1303515
 
5.9%
a 1303513
 
5.9%
9 1302294
 
5.9%
1 1150222
 
5.2%
d 1149956
 
5.2%
f 1149706
 
5.2%
e 1149238
 
5.2%
Other values (7) 8038681
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22064760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 2451640
 
11.1%
4 1762099
 
8.0%
b 1303896
 
5.9%
8 1303515
 
5.9%
a 1303513
 
5.9%
9 1302294
 
5.9%
1 1150222
 
5.2%
d 1149956
 
5.2%
f 1149706
 
5.2%
e 1149238
 
5.2%
Other values (7) 8038681
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22064760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 2451640
 
11.1%
4 1762099
 
8.0%
b 1303896
 
5.9%
8 1303515
 
5.9%
a 1303513
 
5.9%
9 1302294
 
5.9%
1 1150222
 
5.2%
d 1149956
 
5.2%
f 1149706
 
5.2%
e 1149238
 
5.2%
Other values (7) 8038681
36.4%
Distinct79877
Distinct (%)39.8%
Missing412313
Missing (%)67.3%
Memory size35.1 MiB
2025-08-29T09:40:41.727368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters7221492
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61065 ?
Unique (%)30.4%

Sample

1st rowaef53cb0-e89a-516c-88d3-3df6460f2f09
2nd row000ae291-51dd-5d17-bb97-cd0750c7675f
3rd row0001407c-15b5-5a59-80e5-dc427b2cb490
4th row47148fcc-136b-5085-bdfc-71609d1a6a35
5th rowd72cbc23-afe0-55fa-897c-83555e7286e2
ValueCountFrequency (%)
c9b92ffd-a2ed-5787-8b06-f4bf8d291e57 3555
 
1.8%
d30b0ee6-9c54-575f-8cd5-2b3f222da03c 1085
 
0.5%
7b7dbf17-a2ad-54cc-ac66-20995d7f6fba 745
 
0.4%
0cc44ad0-9545-549a-a4ff-90dfc3dd04f6 556
 
0.3%
b293c847-1724-595c-abe7-6a7fdd0f6fa2 473
 
0.2%
bcf61a51-73ad-53d9-893b-06a8587b052b 390
 
0.2%
8e53f01f-e59c-5511-a8fc-63186faaa69e 388
 
0.2%
b53ca40e-954c-5aed-8e1c-27533975d94a 385
 
0.2%
e5c30cf5-d1ea-59e8-a604-a29af2d2bd51 357
 
0.2%
006ad83b-cff8-58b0-bf20-ff8c0fb399c8 327
 
0.2%
Other values (79867) 192336
95.9%
2025-08-29T09:40:41.998633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 802388
 
11.1%
5 570115
 
7.9%
b 440696
 
6.1%
8 429305
 
5.9%
9 425090
 
5.9%
a 417970
 
5.8%
0 391213
 
5.4%
f 388363
 
5.4%
d 386474
 
5.4%
c 381112
 
5.3%
Other values (7) 2588766
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7221492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 802388
 
11.1%
5 570115
 
7.9%
b 440696
 
6.1%
8 429305
 
5.9%
9 425090
 
5.9%
a 417970
 
5.8%
0 391213
 
5.4%
f 388363
 
5.4%
d 386474
 
5.4%
c 381112
 
5.3%
Other values (7) 2588766
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7221492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 802388
 
11.1%
5 570115
 
7.9%
b 440696
 
6.1%
8 429305
 
5.9%
9 425090
 
5.9%
a 417970
 
5.8%
0 391213
 
5.4%
f 388363
 
5.4%
d 386474
 
5.4%
c 381112
 
5.3%
Other values (7) 2588766
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7221492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 802388
 
11.1%
5 570115
 
7.9%
b 440696
 
6.1%
8 429305
 
5.9%
9 425090
 
5.9%
a 417970
 
5.8%
0 391213
 
5.4%
f 388363
 
5.4%
d 386474
 
5.4%
c 381112
 
5.3%
Other values (7) 2588766
35.8%

relationships.company2.data.type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing412313
Missing (%)67.3%
Memory size38.9 MiB
company
200597 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1404179
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcompany
2nd rowcompany
3rd rowcompany
4th rowcompany
5th rowcompany

Common Values

ValueCountFrequency (%)
company 200597
32.7%
(Missing) 412313
67.3%

Length

2025-08-29T09:40:42.071805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-29T09:40:42.119275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
company 200597
100.0%

Most occurring characters

ValueCountFrequency (%)
c 200597
14.3%
o 200597
14.3%
m 200597
14.3%
p 200597
14.3%
a 200597
14.3%
n 200597
14.3%
y 200597
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1404179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 200597
14.3%
o 200597
14.3%
m 200597
14.3%
p 200597
14.3%
a 200597
14.3%
n 200597
14.3%
y 200597
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1404179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 200597
14.3%
o 200597
14.3%
m 200597
14.3%
p 200597
14.3%
a 200597
14.3%
n 200597
14.3%
y 200597
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1404179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 200597
14.3%
o 200597
14.3%
m 200597
14.3%
p 200597
14.3%
a 200597
14.3%
n 200597
14.3%
y 200597
14.3%

domain
Text

Missing 

Distinct93622
Distinct (%)15.7%
Missing14768
Missing (%)2.4%
Memory size44.9 MiB
2025-08-29T09:40:42.281062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length38
Mean length12.805911
Min length4

Characters and Unicode

Total characters7659753
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63872 ?
Unique (%)10.7%

Sample

1st rowunipart.com
2nd rowoosinternational.com
3rd rownwncarousel.com
4th rowgrape.solutions
5th rownwncarousel.com
ValueCountFrequency (%)
amazon.com 13888
 
2.3%
apple.com 9156
 
1.5%
asus.com 3590
 
0.6%
hyundaiusa.com 2462
 
0.4%
marvel.com 2441
 
0.4%
binance.com 2310
 
0.4%
citigroup.com 2155
 
0.4%
abb.com 1709
 
0.3%
lg.com 1577
 
0.3%
marriott.com 1575
 
0.3%
Other values (93611) 557279
93.2%
2025-08-29T09:40:42.573328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 887505
11.6%
c 719553
 
9.4%
. 686221
 
9.0%
m 616923
 
8.1%
a 572742
 
7.5%
e 524858
 
6.9%
r 388423
 
5.1%
n 376760
 
4.9%
i 373381
 
4.9%
s 336504
 
4.4%
Other values (30) 2176883
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7659753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 887505
11.6%
c 719553
 
9.4%
. 686221
 
9.0%
m 616923
 
8.1%
a 572742
 
7.5%
e 524858
 
6.9%
r 388423
 
5.1%
n 376760
 
4.9%
i 373381
 
4.9%
s 336504
 
4.4%
Other values (30) 2176883
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7659753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 887505
11.6%
c 719553
 
9.4%
. 686221
 
9.0%
m 616923
 
8.1%
a 572742
 
7.5%
e 524858
 
6.9%
r 388423
 
5.1%
n 376760
 
4.9%
i 373381
 
4.9%
s 336504
 
4.4%
Other values (30) 2176883
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7659753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 887505
11.6%
c 719553
 
9.4%
. 686221
 
9.0%
m 616923
 
8.1%
a 572742
 
7.5%
e 524858
 
6.9%
r 388423
 
5.1%
n 376760
 
4.9%
i 373381
 
4.9%
s 336504
 
4.4%
Other values (30) 2176883
28.4%

company_name
Text

Missing 

Distinct91492
Distinct (%)15.3%
Missing14875
Missing (%)2.4%
Memory size46.9 MiB
2025-08-29T09:40:42.756587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length199
Median length76
Mean length15.670797
Min length2

Characters and Unicode

Total characters9371685
Distinct characters234
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61651 ?
Unique (%)10.3%

Sample

1st rowUnipart Manufacturing Group
2nd rowOOS International
3rd rowNWN Corporation
4th rowGrape Solutions Plc.
5th rowNWN Corporation
ValueCountFrequency (%)
inc 82173
 
6.1%
group 47046
 
3.5%
ltd 30639
 
2.3%
limited 23107
 
1.7%
university 19803
 
1.5%
of 19753
 
1.5%
amazon.com 13889
 
1.0%
llc 13700
 
1.0%
international 12793
 
1.0%
pty 12053
 
0.9%
Other values (67365) 1066344
79.5%
2025-08-29T09:40:43.088171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
743265
 
7.9%
e 698779
 
7.5%
n 634879
 
6.8%
o 621110
 
6.6%
a 606001
 
6.5%
i 575233
 
6.1%
r 542288
 
5.8%
t 533619
 
5.7%
s 375059
 
4.0%
l 327650
 
3.5%
Other values (224) 3713802
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9371685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
743265
 
7.9%
e 698779
 
7.5%
n 634879
 
6.8%
o 621110
 
6.6%
a 606001
 
6.5%
i 575233
 
6.1%
r 542288
 
5.8%
t 533619
 
5.7%
s 375059
 
4.0%
l 327650
 
3.5%
Other values (224) 3713802
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9371685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
743265
 
7.9%
e 698779
 
7.5%
n 634879
 
6.8%
o 621110
 
6.6%
a 606001
 
6.5%
i 575233
 
6.1%
r 542288
 
5.8%
t 533619
 
5.7%
s 375059
 
4.0%
l 327650
 
3.5%
Other values (224) 3713802
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9371685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
743265
 
7.9%
e 698779
 
7.5%
n 634879
 
6.8%
o 621110
 
6.6%
a 606001
 
6.5%
i 575233
 
6.1%
r 542288
 
5.8%
t 533619
 
5.7%
s 375059
 
4.0%
l 327650
 
3.5%
Other values (224) 3713802
39.6%

ticker
Text

Missing 

Distinct3491
Distinct (%)2.6%
Missing479215
Missing (%)78.2%
Memory size27.7 MiB
2025-08-29T09:40:43.268050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.0011968
Min length2

Characters and Unicode

Total characters1203415
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1282 ?
Unique (%)1.0%

Sample

1st rowASX:VRL
2nd rowLON:INCH
3rd rowASX:CNI
4th rowASX:BEM
5th rowASX:BEM
ValueCountFrequency (%)
nasdaq:amzn 13888
 
10.4%
nsdq:aapl 9156
 
6.8%
otcpk:hymlf 2462
 
1.8%
nyse:c 2155
 
1.6%
swx:abbn 1709
 
1.3%
nasdaq:mar 1575
 
1.2%
nyse:jll 1439
 
1.1%
nasdaq:eei 1329
 
1.0%
otc:fxcof 1237
 
0.9%
nasdaq:eric 1213
 
0.9%
Other values (3483) 97611
73.0%
2025-08-29T09:40:43.564028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 154902
12.9%
N 142212
11.8%
S 140332
11.7%
: 133190
11.1%
E 74504
 
6.2%
D 65906
 
5.5%
Y 57720
 
4.8%
Q 55001
 
4.6%
T 37377
 
3.1%
M 35750
 
3.0%
Other values (33) 306521
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1203415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 154902
12.9%
N 142212
11.8%
S 140332
11.7%
: 133190
11.1%
E 74504
 
6.2%
D 65906
 
5.5%
Y 57720
 
4.8%
Q 55001
 
4.6%
T 37377
 
3.1%
M 35750
 
3.0%
Other values (33) 306521
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1203415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 154902
12.9%
N 142212
11.8%
S 140332
11.7%
: 133190
11.1%
E 74504
 
6.2%
D 65906
 
5.5%
Y 57720
 
4.8%
Q 55001
 
4.6%
T 37377
 
3.1%
M 35750
 
3.0%
Other values (33) 306521
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1203415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 154902
12.9%
N 142212
11.8%
S 140332
11.7%
: 133190
11.1%
E 74504
 
6.2%
D 65906
 
5.5%
Y 57720
 
4.8%
Q 55001
 
4.6%
T 37377
 
3.1%
M 35750
 
3.0%
Other values (33) 306521
25.5%
Distinct91357
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size46.7 MiB
2025-08-29T09:40:43.751554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length194
Median length77
Mean length14.911197
Min length0

Characters and Unicode

Total characters9139222
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61506 ?
Unique (%)10.0%

Sample

1st rowUnipart Manufacturing Group
2nd rowOOS International
3rd rowNWN Corporation
4th rowGrape Solutions Plc
5th rowNWN Corporation
ValueCountFrequency (%)
inc 82173
 
6.2%
group 47046
 
3.5%
ltd 30639
 
2.3%
limited 23107
 
1.7%
university 19920
 
1.5%
of 19753
 
1.5%
llc 13993
 
1.1%
amazoncom 13889
 
1.0%
international 12793
 
1.0%
pty 12053
 
0.9%
Other values (66765) 1056206
79.3%
2025-08-29T09:40:44.091427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
733537
 
8.0%
e 698779
 
7.6%
n 634879
 
6.9%
o 621110
 
6.8%
a 606001
 
6.6%
i 575233
 
6.3%
r 542288
 
5.9%
t 533619
 
5.8%
s 375059
 
4.1%
l 327650
 
3.6%
Other values (53) 3491067
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9139222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
733537
 
8.0%
e 698779
 
7.6%
n 634879
 
6.9%
o 621110
 
6.8%
a 606001
 
6.6%
i 575233
 
6.3%
r 542288
 
5.9%
t 533619
 
5.8%
s 375059
 
4.1%
l 327650
 
3.6%
Other values (53) 3491067
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9139222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
733537
 
8.0%
e 698779
 
7.6%
n 634879
 
6.9%
o 621110
 
6.8%
a 606001
 
6.6%
i 575233
 
6.3%
r 542288
 
5.9%
t 533619
 
5.8%
s 375059
 
4.1%
l 327650
 
3.6%
Other values (53) 3491067
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9139222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
733537
 
8.0%
e 698779
 
7.6%
n 634879
 
6.9%
o 621110
 
6.8%
a 606001
 
6.6%
i 575233
 
6.3%
r 542288
 
5.9%
t 533619
 
5.8%
s 375059
 
4.1%
l 327650
 
3.6%
Other values (53) 3491067
38.2%
Distinct13558
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size41.8 MiB
2025-08-29T09:40:44.291119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length85
Median length0
Mean length5.0555775
Min length0

Characters and Unicode

Total characters3098614
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8139 ?
Unique (%)1.3%

Sample

1st rowUnited Kingdom
2nd row
3rd row
4th rowHungary
5th row
ValueCountFrequency (%)
united 83681
 
18.9%
states 67541
 
15.3%
kingdom 14321
 
3.2%
new 13826
 
3.1%
australia 12583
 
2.8%
india 10237
 
2.3%
california 7952
 
1.8%
york 7531
 
1.7%
texas 4959
 
1.1%
canada 4610
 
1.0%
Other values (10327) 214936
48.6%
2025-08-29T09:40:44.597309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 307773
 
9.9%
t 291749
 
9.4%
e 277950
 
9.0%
274014
 
8.8%
i 252300
 
8.1%
n 243016
 
7.8%
s 155516
 
5.0%
d 150219
 
4.8%
o 124331
 
4.0%
r 114479
 
3.7%
Other values (53) 907267
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3098614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 307773
 
9.9%
t 291749
 
9.4%
e 277950
 
9.0%
274014
 
8.8%
i 252300
 
8.1%
n 243016
 
7.8%
s 155516
 
5.0%
d 150219
 
4.8%
o 124331
 
4.0%
r 114479
 
3.7%
Other values (53) 907267
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3098614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 307773
 
9.9%
t 291749
 
9.4%
e 277950
 
9.0%
274014
 
8.8%
i 252300
 
8.1%
n 243016
 
7.8%
s 155516
 
5.0%
d 150219
 
4.8%
o 124331
 
4.0%
r 114479
 
3.7%
Other values (53) 907267
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3098614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 307773
 
9.9%
t 291749
 
9.4%
e 277950
 
9.0%
274014
 
8.8%
i 252300
 
8.1%
n 243016
 
7.8%
s 155516
 
5.0%
d 150219
 
4.8%
o 124331
 
4.0%
r 114479
 
3.7%
Other values (53) 907267
29.3%

Interactions

2025-08-29T09:40:11.868850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.116855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.498306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.969512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.223035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.601932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:12.127000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.395149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-29T09:40:11.702739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-29T09:40:44.647378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
confidenceamount_normalizedheadcount
confidence1.0000.006-0.001
amount_normalized0.0061.000-0.000
headcount-0.001-0.0001.000
2025-08-29T09:40:44.749833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amount_normalizedcategoryconfidencefinancing_type_normalizedfinancing_type_tagsheadcounthuman_approvedplanningproduct_data.fuzzy_match
amount_normalized1.0000.000-0.0361.0000.000-0.0020.0110.0001.000
category0.0001.0000.1080.0000.1960.0590.3890.1270.018
confidence-0.0360.1081.0000.0510.0160.0020.0370.0420.009
financing_type_normalized1.0000.0000.0511.0000.4461.0000.0470.2981.000
financing_type_tags0.0000.1960.0160.4461.0000.0000.1920.1190.025
headcount-0.0020.0590.0021.0000.0001.0000.0000.0001.000
human_approved0.0110.3890.0370.0470.1920.0001.0000.0900.011
planning0.0000.1270.0420.2980.1190.0000.0901.0000.002
product_data.fuzzy_match1.0000.0180.0091.0000.0251.0000.0110.0021.000

Missing values

2025-08-29T09:40:13.458593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-29T09:40:15.741848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-29T09:40:20.769156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Primary_IDsummarycategoryfound_atconfidencearticle_sentencehuman_approvedplanningamountamount_normalizedassetsassets_tagsawardcontacteffective_dateeventfinancing_typefinancing_type_normalizedfinancing_type_tagsheadcountjob_titlejob_title_tagslocationproductproduct_data.full_textproduct_data.nameproduct_data.release_typeproduct_data.release_versionproduct_data.fuzzy_matchproduct_tagsrecognitionvulnerabilityrelationships.company1.data.idrelationships.company1.data.typerelationships.most_relevant_source.data.idrelationships.company2.data.idrelationships.company2.data.typedomaincompany_nametickercompanylocations
00020f127-3470-4cce-8989-1c79f45da217Unipart Manufacturing Group recognized as Transport and Storage sector winner.recognized_as2022-07-10T20:00:00Z0.8759In addition to being named the safest organisation in the UK, Unipart Logistics won the British Safety Council Chief Adjudicator Award for achieving the highest-scoring application of the 647 received from around the world, and was named Transport and Storage sector winner.FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneUnited KingdomNoneNoneNoneNoneNoneNoneTransport and Storage sector winnerNone000bd323-1bf8-5c7a-9941-e6c155c29d10companyd172abc1-3755-4cef-946e-7de944806e7dNaNNaNunipart.comUnipart Manufacturing GroupNoneUnipart Manufacturing GroupUnited Kingdom
1009be1ff-6cfb-4e9f-a415-69baf71f47f3OOS International received award two safety awards on Jan 1st '18.receives_award2019-12-19T10:45:17Z0.9497Since then OOS International has been an active member of the IADC and received two safety awards in 2018.FalseFalseNoneNaNNonetwo safety awardsNone2018-01-01NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneNoneNone000ff896-4292-5b15-9c81-8bf4d76c10d7company58c0d5fd-068d-4bab-8ac4-47e19bbdf091NaNNaNoosinternational.comOOS InternationalNoneOOS International
201444124-7375-4f03-8879-eb8200b31504NWN Corporation received award Global Winner for 2022 Microsoft Meetings, Calling & Devices for Microsoft Teams Partner of the Year Award on Jun 28th '22.receives_award2022-07-12T20:00:00Z0.6887As a result, with nearly 400 nominees from over 100 countries, NWN Corporation is pleased to announce NWN Carousel was recognized as a Global Winner for 2022 Microsoft Meetings, Calling & Devices for Microsoft Teams Partner of the Year Award.FalseFalseNoneNaNNoneGlobal Winner for 2022 Microsoft Meetings, Calling & Devices for Microsoft Teams Partner of the Year AwardNone2022-06-28NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneNoneNone000d8a9c-882c-57f2-8b4c-2afc786d0fa1companyef330a38-8624-41c1-8b75-d1b96e7dbd45NaNNaNnwncarousel.comNWN CorporationNoneNWN Corporation
3031a304c-29ca-415e-a815-e9c915896540Grape Solutions Plc. is developing Mobiliti app on Jan 1st '18.is_developing2023-04-02T22:00:00Z0.5987MVM Mobiliti and Grape Solutions have been working together since 2018 to develop the Mobiliti app, becoming the most downloaded electric car charging app in Hungary, with more than 215,000 charging stations in 39 countries.FalseFalseNoneNaNNoneNoneNone2018-01-01NoneNoneNone0NoneHungaryMobiliti appMobiliti appNoneNoneNoneTruemobile, online_technologyNoneNone0008b75f-9d15-54ae-b70a-52301945e397company0525807d-6ff6-44a0-9c36-8be3afceba5bNaNNaNgrape.solutionsGrape Solutions Plc.NoneGrape Solutions PlcHungary
4037783ca-f3f7-4782-8a81-df3cae1ac936NWN Corporation launched two new kits, At-Home Essentials and Office Collaboration Room-as-a-Service on Apr 13th '22.launches2022-04-13T01:02:36Z0.7180NWN Carousel, the leading integrated cloud communications service provider, today announced two new kits, At-Home Essentials and Office Collaboration Room-as-a-Service, for organizations to manage the accelerating demands of the hybrid workplace with connectivity, security, devices and visual collaboration.FalseFalseNoneNaNNoneofficeNoneNone2022-04-13NoneNoneNone0NonesupportNonetwo new kits, At-Home Essentials and Office Collaboration Room-as-a-Servicetwo new kits, At-Home Essentials and Office Collaboration Room-as-a-ServiceNoneNoneNoneTrueNoneNone000d8a9c-882c-57f2-8b4c-2afc786d0fa1company16061c55-111d-496a-9e3e-837dddc3454bNaNNaNnwncarousel.comNWN CorporationNoneNWN Corporation
503d14654-015f-4efa-b986-05a6b032e8eaGems Sensors, Inc. launches Model 3800.launches2015-03-24T23:00:00Z0.5464Gems Sensors & Controls announces the global market launch of its Model 3800 and 3820 Series of reliable, accurate, compact OEM pressure transmitters and switches for hazardous area and other hostile environments.FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneNoneModel 3800Model 3800NoneNoneNoneTrueNoneNone000875e7-0d87-5fe3-9a42-f80baa9f6f01company52b18c2e-9c26-4280-b3a4-0035340e2de2NaNNaNgemssensors.comGems Sensors, Inc.NoneGems Sensors Inc
604143a02-d0a8-4079-97f1-35bc1497bfb9Neuroblastoma Australia Incorporated receives financing of $155K in donations.receives_financing2020-09-08T00:25:02Z0.9115So far Neuroblastoma Australia has received $155,000 in donations which is just incredible.FalseFalse$155,000155000.0NoneNoneNone1970-01-01NonedonationsNonedonation0NoneNoneNoneNoneNoneNoneNoneNoneNoneNone0007bc22-874d-5770-afbe-919728e9c3a2company3810a5f8-f64e-4987-9cc9-bb58c3e2bb13NaNNaNneuroblastoma.org.auNeuroblastoma Australia IncorporatedNoneNeuroblastoma Australia Incorporated
70493a8e0-6cb2-4a0c-9cff-9076252a963dNWN Corporation recognized as Best Place to Work on Jan 1st '22.recognized_as2023-05-01T18:00:00Z0.9673He’s also leading a company that makes its own employees happy: NWN Carousel was recognized by Comparably as a “Best Place to Work” in 2022.”FalseFalseNoneNaNNoneNoneNone2022-01-01NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneBest Place to WorkNone000d8a9c-882c-57f2-8b4c-2afc786d0fa1companyfcc901f9-d9ec-43c4-af27-b7cdca10acfaNaNNaNnwncarousel.comNWN CorporationNoneNWN Corporation
80583b4eb-d105-492b-98f9-a4c419b3f5c7NWN Corporation hires Jim Sullivan as CEO and chairman.hires2019-05-09T14:04:00Z0.4973Solution provider NWN Corp. has appointed a new CEO, Jim Sullivan, who had been serving as president of data management software company Actifio.FalseFalseNoneNaNNoneNoneJim Sullivan1970-01-01NoneNoneNone0CEO and chairmandirectorsNoneNoneNoneNoneNoneNoneNoneNoneNone000d8a9c-882c-57f2-8b4c-2afc786d0fa1companyadbaedb0-b528-415b-8e4f-d038ebbbabd5NaNNaNnwncarousel.comNWN CorporationNoneNWN Corporation
90627a827-df60-4ace-9b79-bc98c9bf4c59Neuromersiv Pty Ltd receives financing of $1M in grant funding.receives_financing2016-09-07T20:00:00Z1.0000Neuromersiv secures $1 million to advance MedTech for stroke, spinal cord & brain injury survivors.FalseFalse$1m1000000.0NoneNoneNone1970-01-01Nonegrant fundingNoneequity, grant0NoneSydney, AustraliaNoneNoneNoneNoneNoneNoneNoneNone0006cb4b-9b5e-57b1-b30b-f8deb2a91e47company4efd7b5a-93b8-4434-8a5e-bb0197a55b4cNaNNaNneuromersiv.comNeuromersiv Pty LtdNoneNeuromersiv Pty LtdSydney Australia
Primary_IDsummarycategoryfound_atconfidencearticle_sentencehuman_approvedplanningamountamount_normalizedassetsassets_tagsawardcontacteffective_dateeventfinancing_typefinancing_type_normalizedfinancing_type_tagsheadcountjob_titlejob_title_tagslocationproductproduct_data.full_textproduct_data.nameproduct_data.release_typeproduct_data.release_versionproduct_data.fuzzy_matchproduct_tagsrecognitionvulnerabilityrelationships.company1.data.idrelationships.company1.data.typerelationships.most_relevant_source.data.idrelationships.company2.data.idrelationships.company2.data.typedomaincompany_nametickercompanylocations
6207759cb896d4-b0b0-41bc-ab1b-d30528432f45HBC launches SOREL Footwear pop-up shops.launches2023-09-23T22:00:00Z0.0000Hudson's Bay is announcing the arrival of exclusive SOREL Footwear pop-up shops at its Hudson's Bay location at Yorkdale Shopping Centre in Toronto as well as its flagship locations in Vancouver and Montreal.FalseFalseNoneNaNNoneretailNoneNone1970-01-01NoneNoneNone0NoneNoneSOREL Footwear pop-up shopsSOREL Footwear pop-up shopsNoneNoneNoneTrueNoneNonef7120c57-9de9-56ae-8c6b-d770d0a97461company73e43540-1859-421d-872f-61c5eb207bc1NaNNaNhbc.comHBCNoneHBC
6207769d2580bc-9f25-4349-8b52-f8ac9e8452c6Access Industries Inc. invested into Ada Health $47M on Oct 31st '17.invests_into2017-10-30T19:00:22Z0.7296Today, Ada Health announced that it has raised $47 million in venture capital in a funding round led by Access Industries, June Fund, and Berlin-based Cumberland VC.TrueFalse$47 million4.700000e+07NoneNoneNone2017-10-31NoneNoneNone0NoneLondon, United KingdomNoneNoneNoneNoneNoneNoneNoneNonef70b5d8f-24e5-5c3b-84dc-5596d32dccc7companya04b69fa-0380-41d8-9369-693326f56d0960f68693-a447-59a0-8cec-6de8ffae3036companyaccessindustries.comAccess Industries Inc.NoneAccess Industries IncLondon United Kingdom
6207779d3d5f7b-bbec-434f-aab3-770bcb34cf79Maxeon Solar Technologies Ltd. launches Sustainability Report.launches2022-06-30T05:00:00Z0.5786Maxeon Solar Technologies, Ltd. (NASDAQ: MAXN ), a global leader in solar innovation and channels, today announced the release of its Sustainability Report for the year 2021 (Sustainability Report).FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneSINGAPORESustainability ReportSustainability ReportNoneNoneNoneTruereportNoneNonef709c720-e19b-51f3-aa9d-86fdb73bd9b9companyc63b5d1d-894f-4bc4-a96a-5b3a0be8ea4eNaNNaNmaxeon.comMaxeon Solar Technologies Ltd.NASDAQ:MAXNMaxeon Solar Technologies LtdSINGAPORE
6207789e42289e-6a00-4017-aff9-c192f13e6ba2Access Industries Inc. acquired Warner for $3.3B on Jan 1st '11.acquires2020-02-06T04:00:00Z0.6556Access Industries acquired Warner Music Group for US$3.3bn in 2011.FalseFalse$3.3 billion3.300000e+09NoneNoneNone2011-01-01NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneNoneNonef70b5d8f-24e5-5c3b-84dc-5596d32dccc7companyc9d64c5a-7a0f-4612-9a85-0c7bebe183e8e44a98ca-f7e3-58d9-9ea9-b0d26610cefbcompanyaccessindustries.comAccess Industries Inc.NoneAccess Industries Inc
6207799e5c037a-0bb5-4aa4-bcd1-a022120872c1Mineral Fusion partners with Dress for Success.partners_with2023-06-23T16:34:15Z0.4978Mineral Fusion is a pledge partner with Dress for Success in the Your Hour, Her Power campaign.FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneNoneNoneed44a6e9-a97f-50e2-8f0b-f94d90eb0cc3companyc9cede29-f766-4278-9b95-48fc678c7e08f712ba14-7ae6-51c6-9ce9-6d9b7f1947d6companymineralfusion.comMineral FusionNoneMineral Fusion
6207809eb89308-1185-4673-bd0b-71b49793e597Cityofhobart partners with Dress for Success.partners_with2024-03-03T23:00:00Z0.3128As International Women's Day (IWD) draws near, the City of Hobart reaffirms its commitment to gender equality and female empowerment through its ongoing partnership with Dress for Success, an organization dedicated to assisting women in their employment journey.FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneNoneNonea7b8ea84-323f-5841-be92-4549ae87b975company5a6492cd-4940-406b-b01c-9c4b626bd492f712ba14-7ae6-51c6-9ce9-6d9b7f1947d6companycityofhobart.orgCityofhobartNoneCityofhobart
6207819fb29c5d-ddfd-4eb1-9827-e544fa1e4591Maxeon Solar Technologies Ltd. has issues with securities fraud or other unlawful business practices.has_issues_with2024-07-18T18:20:17Z0.5880The class action concerns whether Maxeon and certain of its officers and/or directors have engaged in securities fraud or other unlawful business practices.FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneNoneNoneNoneNoneNoneNoneNoneNonesecurities fraud or other unlawful business practicesf709c720-e19b-51f3-aa9d-86fdb73bd9b9company5eaf97cc-65cf-4f8d-a2c0-7d7951bee748NaNNaNmaxeon.comMaxeon Solar Technologies Ltd.NASDAQ:MAXNMaxeon Solar Technologies Ltd
620782a0cc659c-f18a-4841-bd56-412bf79e3bb3HBC launched update to Companies Creditors Arrangement Act on Mar 21st '25.launches2025-03-25T00:00:00Z0.0000On Friday, the Hudson's Bay Company announced an update to its Companies' Creditors Arrangement Act (CCAA), which it filed earlier this month.FalseFalseNoneNaNNoneNoneNone2025-03-21NoneNoneNone0NoneNoneupdate to Companies Creditors Arrangement Actupdate to its Companies' Creditors Arrangement ActNoneupdateNoneTrueNoneNonef7120c57-9de9-56ae-8c6b-d770d0a97461companya1b74fea-9eba-4f6b-8e93-d21668e6aad7NaNNaNhbc.comHBCNoneHBC
620783a3c344d0-0e68-4a99-90c9-ab2c90253e67Xpansiv CBL Holding Group receives financing of $25M in conditional debt financing.receives_financing2021-07-27T16:20:25Z0.7883In conjunction with the convertible note, Xpansiv has also secured a further US$25M of conditional debt financing from a global investment bank to support future M&A transactions.FalseFalseUS$25M2.500000e+07NoneNoneNone1970-01-01Noneconditional debt financingNonedebt0NoneNoneNoneNoneNoneNoneNoneNoneNoneNonef70cd42a-379b-5362-9f58-f91f6e079adccompany4bb794cd-48d3-4c90-a5bc-50e4b277f1c8NaNNaNxpansiv.comXpansiv CBL Holding GroupNoneXpansiv CBL Holding Group
620784a3d9d83c-47d6-4c5a-b87c-ae3e52484f01Mirada PLC launches Iris TV Everywhere Solution at TV Connect.launches2019-09-05T11:29:00Z0.7016Mirada will announce its newest release of Iris TV Everywhere Solution at TV Connect in London.FalseFalseNoneNaNNoneNoneNone1970-01-01NoneNoneNone0NoneLondon, United KingdomIris TV Everywhere Solution at TV ConnectIris TV Everywhere Solution at TV ConnectNoneNoneNoneTrueNoneNonef7099284-191e-584f-b598-2128718fd969company3da0d195-3915-4897-8467-a9678f594329NaNNaNmirada.tvMirada PLCLON:MIRAMirada PLCLondon United Kingdom